Neural Network Datasets ----------------------- Function Fitting, Function approximation and Curve fitting. Function fitting is the process of training a neural network on a set of inputs in order to produce an associated set of target outputs. Once the neural network has fit the data, it forms a generalization of the input-output relationship and can be used to generate outputs for inputs it was not trained on. simplefit_dataset - Simple fitting dataset. abalone_dataset - Abalone shell rings dataset. bodyfat_dataset - Body fat percentage dataset. building_dataset - Building energy dataset. chemical_dataset - Chemical sensor dataset. cho_dataset - Cholesterol dataset. engine_dataset - Engine behavior dataset. vinyl_dataset - Vinyl bromide dataset. ---------- Pattern Recognition and Classification Pattern recognition is the process of training a neural network to assign the correct target classes to a set of input patterns. Once trained the network can be used to classify patterns it has not seen before. simpleclass_dataset - Simple pattern recognition dataset. cancer_dataset - Breast cancer dataset. crab_dataset - Crab gender dataset. glass_dataset - Glass chemical dataset. iris_dataset - Iris flower dataset. ovarian_dataset - Ovarian cancer dataset. thyroid_dataset - Thyroid function dataset. wine_dataset - Italian wines dataset. digitTrain4DArrayData - Synthetic handwritten digit dataset for training in form of 4-D array. digitTrainCellArrayData - Synthetic handwritten digit dataset for training in form of cell array. digitTest4DArrayData - Synthetic handwritten digit dataset for testing in form of 4-D array. digitTestCellArrayData - Synthetic handwritten digit dataset for testing in form of cell array. digitSmallCellArrayData - Subset of the synthetic handwritten digit dataset for training in form of cell array. japaneseVowelsTrainData - Japanese vowels training dataset for time series classification. japaneseVowelsTestData - Japanese vowels test dataset for time series classification. ---------- Clustering, Feature extraction and Data dimension reduction Clustering is the process of training a neural network on patterns so that the network comes up with its own classifications according to pattern similarity and relative topology. This is useful for gaining insight into data, or simplifying it before further processing. simplecluster_dataset - Simple clustering dataset. The inputs of fitting or pattern recognition datasets may also clustered. ---------- Input-Output Time-Series Prediction, Forecasting, Dynamic modeling Nonlinear autoregression, System identification and Filtering Input-output time series problems consist of predicting the next value of one time-series given another time-series. Past values of both series (for best accuracy), or only one of the series (for a simpler system) may be used to predict the target series. simpleseries_dataset - Simple time-series prediction dataset. simplenarx_dataset - Simple time-series prediction dataset. exchanger_dataset - Heat exchanger dataset. maglev_dataset - Magnetic levitation dataset. ph_dataset - Solution PH dataset. pollution_dataset - Pollution mortality dataset. refmodel_dataset - Reference model dataset robotarm_dataset - Robot arm dataset valve_dataset - Valve fluid flow dataset. ---------- Single Time-Series Prediction, Forecasting, Dynamic modeling, Nonlinear autoregression, System identification, and Filtering Single time-series prediction involves predicting the next value of a time-series given its past values. simplenar_dataset - Simple single series prediction dataset. chickenpox_dataset - Monthly chickenpox instances dataset. ice_dataset - Global ice volume dataset. laser_dataset - Chaotic far-infrared laser dataset. oil_dataset - Monthly oil price dataset. river_dataset - River flow dataset. solar_dataset - Sunspot activity dataset See also nndemos, nntextdemos.

abalone_dataset Abalone shell rings dataset. Function fitting is the process of training a neural network on a set of inputs in order to produce an associated set of target outputs. Once the neural network has fit the data, it forms a generalization of the input-output relationship and can be used to generate outputs for inputs it was not trained on. This dataset can be used to train a neural network to estimate the number of abalone shell rings from several attributes of the shell. LOAD abalone_dataset.MAT loads these two variables: abaloneInputs - an 8x4177 matrix defining eight attributes for 4177 different shells. 1. Sex: M, F, and I (infant) 2. Length 3. Diameter 4. Height 5. Whole weight 6. Shucked weight 7. Viscera weight 8. Shell weight abaloneTargets - a 1x4177 matrix of ring counts for each shell. The number of rings of an abolone shell is a useful value to estimate as it can be used to compute the age by adding 1.5. [X,T] = abalone_dataset loads the inputs and targets into variables of your own choosing. For an intro to fitting with the Neural Fitting app click "Load Example Data Set" in the second panel and pick this dataset. Here is how to design a fitting neural network with 10 hidden neurons with this data at the command line. See fitnet for more details. [x,t] = abalone_dataset; net = fitnet(10); net = train(net,x,t); view(net) y = net(x); See also nftool, fitnet, nndatasets. ---------- This data is available from the UCI Machine Learning Repository. http://mlearn.ics.uci.edu/MLRepository.html Murphy,P.M., Aha, D.W. (1994). UCI Repository of machine learning databases [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science. Donated to the repository by Sam Waugh.

Variables: Cement_component1__kgInAM_3Mixture_: 1030×1 double Properties: Description: 原始列标题: 'Cement (component 1)(kg in a m^3 mixture)' Values: Min 102 Median 272.9 Max 540 BlastFurnaceSlag_component2__kgInAM_3Mixture_: 1030×1 double Properties: Description: 原始列标题: 'Blast Furnace Slag (component 2)(kg in a m^3 mixture)' Values: Min 0 Median 22 Max 359.4 FlyAsh_component3__kgInAM_3Mixture_: 1030×1 double Properties: Description: 原始列标题: 'Fly Ash (component 3)(kg in a m^3 mixture)' Values: Min 0 Median 0 Max 200.1 Water_component4__kgInAM_3Mixture_: 1030×1 double Properties: Description: 原始列标题: 'Water (component 4)(kg in a m^3 mixture)' Values: Min 121.75 Median 185 Max 247 Superplasticizer_component5__kgInAM_3Mixture_: 1030×1 double Properties: Description: 原始列标题: 'Superplasticizer (component 5)(kg in a m^3 mixture)' Values: Min 0 Median 6.35 Max 32.2 CoarseAggregate_component6__kgInAM_3Mixture_: 1030×1 double Properties: Description: 原始列标题: 'Coarse Aggregate (component 6)(kg in a m^3 mixture)' Values: Min 801 Median 968 Max 1145 FineAggregate_component7__kgInAM_3Mixture_: 1030×1 double Properties: Description: 原始列标题: 'Fine Aggregate (component 7)(kg in a m^3 mixture)' Values: Min 594 Median 779.51 Max 992.6 Age_day_: 1030×1 double Properties: Description: 原始列标题: 'Age (day)' Values: Min 1 Median 28 Max 365 ConcreteCompressiveStrength_MPa_Megapascals_: 1030×1 double Properties: Description: 原始列标题: 'Concrete compressive strength(MPa, megapascals) ' Values: Min 2.331807832 Median 34.44277358 Max 82.5992248